Energy Forecasting and Performance modelling wind and Solar farms/Forecasting techniques for renewable energy sources:
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Performance analysis and Techno-economic impact on grid operation 1).The unpredictable nature of the high penetration of RE specifically wind and PV power may rapidly deteriorate both the reliability and economic performance of existing power systems. This project focuses on developing statistical learning methods appropriate for forecasting PV generation. Existing PV power data with spatial and temporal correlation will be utilized to understand the underlying statistical properties of PV generation. Using these properties, short-term time-series forecasting tools will be developed in order to provide useful forecasts and uncertainty measures. The resulted high-performance forecasting methods along with the knowledge of basic household load profiles will allow for a more accurate quantification of active power reserve requirements necessitated by the future PV-rich distribution systems. This will be achieved by analyzing the impact of the uncertainty on reserve requirements given the technical constraints and control capabilities of a distribution network. This forecasting approach is a key step towards the development of a data-driven decision-making framework for power system operation. 2) Candidates also need to draw similar study and conclusion on large wind farms energy forecasting models and derive the impact study on its integration and load balancing at city and national grid
Power in Numbers
AI-ML /Big Data /Python Programming/ Basic Knowledge of electricity and RE dynamics; Strong in quantitative and qualitative approach of handling problem statements and an urge to learn and contribute to renewable energy space